In general, sensors typically receive signal data and measure some set of signal parameters using appropriate signal measurement techniques. A channelization tracking system is usually included which groups signal data with similar parameters and provides some indication of parameter changes over time. The system will attempt to infer other attributes based on the grouped parameter data which can be used to classify and identify that signal source. Nearly all sensors employ fairly standard signal processing algorithms for parameter measurement and tracking functions which may be implemented with either digital or analog devices, or a combination of both. Heuristic pattern matching techniques are typically used for classification and identification. These may also be used to perform tracking and pre-processing functions when there is insufficient data for classical methods such as Kalman filters to be effective. While these devices were designed with a particular type of sensor in mind, i.e. a radar ESM system, they will also be applicable to a broad range of sensor applications with minor modifications. The particular systems described in the present application utilize sensors which passively intercept and analyze pulsed radio frequency (RF) signals in the microwave bands.
The receivers in radar electronic support measures (RESM) systems can generate considerable quantities of data in dense electromagnetic signal environments with this data being generally digitized and formatted into pulse descriptor words (PDWs). These PDWs include parameters such as "time-of-arrival" (TOA), "pulse amplitude", "radio frequency" (RF), "angle-of-arrival" (AOA), and "pulse width". The time history of the data corresponding to a discrete emitter can be processed to yield additional information such as the "pulse repetition interval" (PRI) and the "scan type" and "period" of the transmitting antenna. These signal parameters can then be compared to values stored in libraries in order to identify the emitters of those signals.
The processing of all this raw RESM data is complicated since the PDW time histories of a number of emitters is interleaved. It is therefore desirable to perform a form of coarse deinterleaving of the raw data using information such as frequency, angle-of-arrival and pulse width. Ideally, using this information, pulse data from most emitters can be separated based on the uniqueness of clusters in these dimensions. High peak data rates are possible which will result in a requirement for the use of very high speed hardware in order to filter the data. The use of highly parallel programmable multi-processor architectures has been proposed. However, the relatively simple and repetitive nature of the filtering algorithm favours the use of specialized high speed hardware which can minimize the time overheads associated with the control and intercommunication functions.
The PDW data is generally transferred over a specialized signal data bus to a pre-processor. In these systems, the function of the pre-processor which follows the receiver unit is to deinterleave the PDWs into one or more separate emitter tracks or pulse trains. This is essentially a correlation or sorting process, often involving two stages. In the first stage, data is selectively captured and stored based on programmable parameter filters. These parameter filters are programmed with parameter ranges or windows corresponding to emitters which were previously detected and classified. PDWs whose parameter fields fall within the ranges which have been set for a particular filter are captured by that filter. This stage of the pre-processor is usually implemented with digital hardware in order to keep up with the high data rate of the incoming PDWs. The second stage, which may also be implemented in hardware but is more often a software process, involves more fine-grained analysis of samples of the incoming data. In this second stage, any data collected from existing filters is analyzed to verify that the signal parameters associated with an emitter track have not changed significantly. If a significant change is observed, the parameter ranges associated with the corresponding parameter filters are adjusted to track the change. Data which is not captured by any of the existing parameter filters is sampled and analyzed in the second stage to determine how many new emitters are represented in the sample, i.e. emitters for which no corresponding parameter window previously existed. This data is then grouped and sorted into buffers corresponding to new emitter tracks.
The main functions of the pre-processor are therefore to:
(a) selectively capture and sort data from existing tracks; PA1 (b) acquire new signals as they enter the environment; PA1 (c) monitor existing signals for any parameter changes; and PA1 (d) reduce the flow of data to subsequent processing stages. PA1 (a) signal acquisition - the formation of a new track channel which will be used to capture subsequent parameter measurements from any new signal source which falls within the viewport; PA1 (b) data collection - a facility to collect data which is associated with each track channel in time order; PA1 (c) collision detection - a capability to detect when clusters being tracked overlap so that the track channels associated with these overlapping clusters can be merged; PA1 (d) adaptability - capabilities to modify the parameter window of-each track channel, split a track channel so that data collection can occur independently through two separate parameter windows, force track channels to merge and remove track channels; and PA1 (e) automatic tracking - a capability to adjust the tracking window without external direction to follow the cluster of signal data in one or more dimensions. This feature may be optional depending on the variability of the received data.
Any ESM system which performs emitter tracking must have a subsystem which keep parameter filters approximately centred on the parameters of the incoming PDW data. Furthermore, any PDW data which is designated by the pre-processor as a new emitter track must be analyzed and classified. This function may be implemented within the tracking subsystem or may be done by a separate classification and identification module. The classification process is aimed at characterizing the behaviour over time of the pulse train as a whole, as opposed to the pre-processor which considers only single pulses. Pulse train characteristics which are measured in this classification process include items such as the structure of the pulse repetition intervals (PRIs), the pattern of any radio frequency agility and the emitter's scan pattern.
An acquisition subsystem sets up new parameter filters for new signal sources. These filters will intercept any further incoming data that should be associated with a new track. New tracks may be identified with one or more generic emitter types by matching their classification with an emitter database containing characteristic parameters for a large number of known emitters. Emitter tracking and classification as well as identification subsystems are both usually implemented as software processes.
New emitter tracks and updates of old tracks are passed on for higher level processing. This will usually involve maintaining a list of currently active emitters in the environment, at a minimum, and forwarding this information to an ESM operator display or a higher level Command and Control System. Other functions may be included such as platform-to-emitter correlation, displays showing emitter/platform bearing and motion, interactive programs which allow an operator to perform additional data analysis and so forth. In addition, there are generally a number of system control functions implemented at this level, mainly to provide interfaces between the lower levels of ESM processing, the ESM operator and the Command and Control System.
A parametric tracker is a type of pre-processor which also includes, as an integral component, a tracking subsystem that keep parameter filters centred on the incoming PDWs data in addition to performing functions (a) to (d).
Inputs to a parametric tracking subsystem are a time ordered stream of PDWs from an ESM receiver. Each PDW is the result of an associated sensor measurement for an individual pulse and consists of an ordered set of digitized parameter measurements. There can also be a number of discrete fields which describe other attributes of the signal such as the stability of a parameter during the transmission duration. Measured parameter values associated with successive emissions from any one signal source may vary due to small errors introduced by environmental noise, variations in the equipment or the propagation environment. This variability of parameter values complicates the operation of a tracker and can degrade its performance.
The environment in which signal sources reside may be considered as a multi-dimensional parameter space in which clusters of measured parameter values represent the signal sources. A cluster would then be a particular grouping of PDWs in the dimensions of interest based on some nearness criterion. The parametric tracker task may then be described as collecting and processing data from these clusters of signal data for as long as their sources persist in the environment. However, clusters in one or more dimensions may change in size or may translate as a function of time. Variations in the physics of the situation can result in slow changes in a parameter value and it is desirable to track these changes in parameter space. This will not only permit continued collection of data from an associated signal source but will also avoid misinterpreting that data as being from a number of different sources appearing sequentially in time. Clusters may also move in such a manner as to collide and overlap with other clusters. In that case, the data collection process may have to be altered to collect data from a larger cluster encompassing those clusters involved in the collision.
An ideal parametric tracker should maintain a set of discrete track channels, each of which maintains a window in a parameter space that can be tuned and adjusted to track a particular signal cluster. That portion of a parameter space which is of interest to the tracker is called a viewport. The functions of an ideal parametric tracker are:
In prior art parametric trackers, measured signal data in the form of PDWs are applied to an input first-in-first-out (FIFO) buffer. As each PDW is obtained from the FIFO buffer, the parameters of interest are extracted and tested against a set of corresponding minimum and maximum limits in each tracking filter. For filters which detect a match, the entire PDW is stored in corresponding data buffers or, alternatively, stored in a single large buffer using linked list techniques. The tracking filter plus the buffer constitute one track channel.
If no filter claims a PDW, it is applied to an acquisition filter. If the parameters of interest for this PDW fall within its viewport limits, i.e. the limits of the portion of parameter space being intercepted by this parametric tracker, it is stored in an acquisition buffer. The data collected in this buffer is then analyzed by a clustering algorithm to estimate the dimensions of one or more clusters that contain the data. Each cluster is formed using a nearness criterion where a data point is said to belong to a cluster if it is within a pre-set minimum distance in each parameter dimension from a predetermined point. Tracking filters are tuned and enabled on these clusters once the analysis of an acquisition data segment is complete. One example of an implementation of such a system is described by Robert J. Inkol in U.S. Pat. No. 4,879,561 which was issued on Nov. 7, 1989.
A number of disadvantages exist with these previous systems such as that more data from the same signal source may be captured by the acquisition buffer before new tracking filters can be set up due to the processing delay associated with the acquisition cluster analysis. Since it is desirable that all data from an individual signal source be stored together in time-sorted order, all data associated with a new signal which falls in the acquisition buffer must be properly associated with the new track channel and stored properly in its data buffer using some standard sorting algorithm. This is complicated to implement. If the buffers associated with each filter are simple circular buffers occupying fixed blocks of address space in a memory, provision must be made to read PDW data from the acquisition buffer to the new track buffer before the latter receives any data from the newly created tracking filter.
A further disadvantage is that checks for overlapping track channel windows must be periodically performed in software which will cause a delay between the real-time collision of windows and the subsequent track channel merge operation. Since more data may fall into the overlap region during this delay, it will be inappropriately stored in all track channels involved in the overlap. These duplicate PDWs must then be removed when the track data is analyzed.
Another disadvantage is that as signals fall outside the hard limits implemented by the tracking windows, they must be re-acquired through the acquisition buffer, a new track channel must be set up and the old track channel terminated. Sophisticated software is required to determine that the new and old tracks represent the same emitter and provide the appearance of continuity to an operator. Any loss of continuity will frequently affect the quality of classification and identification processing as well as waste CPU time.
Another disadvantage of previous systems is that signals from the same emitter which fall into different regions of parameter space, for instance signals from frequency agile radars, will appear in different track channels. Even after such an emitter has been properly classified and identified, no hardware support is provided in these systems to merge the data in time-sorted order. This must be done by appropriate software processes in subsequent stages of the processing.
A further disadvantage is that PDWs from a new emitter which fall into an existing tracking window cannot be detected without periodically re-analyzing the data buffer. Brief illuminations may, as a result, be undetected and the response time will, in general, be slow. One approach which has been used to alleviate this problem is to use TOA as a tracking parameter. Since the PRIs of pulsed radar signals are often characteristic of that signal, time-gating techniques can then be used to separate PDWs from two or more emitters based on PRI patterns. Current designs require that the time-gating algorithms be implemented directly in hardware. However, a problem with this approach is that the signal must be re-acquired if the time-gating technique fails with subsequent loss of continuity.